{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:XGSYIIV376KNSG2P5EF6GBHUBS","short_pith_number":"pith:XGSYIIV3","canonical_record":{"source":{"id":"2001.04585","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-01-14T02:03:01Z","cross_cats_sorted":["cs.SD","eess.SP"],"title_canon_sha256":"e99a6286768f2c1f23441adb596a6e778bfefa4b2c5e4903003922eac346a367","abstract_canon_sha256":"9ff0b04d66b4adf3857698930b5240b312c5c0fc7c1b44e63c71e4c414526e4e"},"schema_version":"1.0"},"canonical_sha256":"b9a58422bbff94d91b4fe90be304f40cb70bf66ce24109cd0a04f4bd5281036b","source":{"kind":"arxiv","id":"2001.04585","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.04585","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"arxiv_version","alias_value":"2001.04585v1","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.04585","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"pith_short_12","alias_value":"XGSYIIV376KN","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"XGSYIIV376KNSG2P","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"XGSYIIV3","created_at":"2026-07-05T00:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:XGSYIIV376KNSG2P5EF6GBHUBS","target":"record","payload":{"canonical_record":{"source":{"id":"2001.04585","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-01-14T02:03:01Z","cross_cats_sorted":["cs.SD","eess.SP"],"title_canon_sha256":"e99a6286768f2c1f23441adb596a6e778bfefa4b2c5e4903003922eac346a367","abstract_canon_sha256":"9ff0b04d66b4adf3857698930b5240b312c5c0fc7c1b44e63c71e4c414526e4e"},"schema_version":"1.0"},"canonical_sha256":"b9a58422bbff94d91b4fe90be304f40cb70bf66ce24109cd0a04f4bd5281036b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:33:27.190633Z","signature_b64":"Ss5x6tMlxbCAAipAFjYeW3n8mpVSKGH+k9cgiGWQCBk7aRpW35Lb8dO1IYDOt8PS2dbMMeUQjuu4DK+8t/QLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9a58422bbff94d91b4fe90be304f40cb70bf66ce24109cd0a04f4bd5281036b","last_reissued_at":"2026-07-05T00:33:27.190281Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:33:27.190281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2001.04585","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:33:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JdXHph2bW7Fy9YoszsUhY6BsPsMdYPDY5Cxh93O7merip4GwUoTmlEJ7kmsRoZNYYNAY1N8Kd7RbX333IUMDBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:53:00.437550Z"},"content_sha256":"83b732c12f645d81b5fc1e7ec4266900d370c387eefff470bcfe03ff0f21d27a","schema_version":"1.0","event_id":"sha256:83b732c12f645d81b5fc1e7ec4266900d370c387eefff470bcfe03ff0f21d27a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:XGSYIIV376KNSG2P5EF6GBHUBS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gaussian speaker embedding learning for text-independent speaker verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.SP"],"primary_cat":"eess.AS","authors_text":"Bin Gu, Wu Guo","submitted_at":"2020-01-14T02:03:01Z","abstract_excerpt":"The x-vector maps segments of arbitrary duration to vectors of fixed dimension using deep neural network. Combined with the probabilistic linear discriminant analysis (PLDA) backend, the x-vector/PLDA has become the dominant framework in text-independent speaker verification. Nevertheless, how to extract the x-vector appropriate for the PLDA backend is a key problem. In this paper, we propose a Gaussian noise constrained network (GNCN) to extract xvector, which adopts a multi-task learning strategy with the primary task classifying the speakers and the auxiliary task just fitting the Gaussian "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.04585","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2001.04585/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:33:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"POrZ64fa8StTMQA6QyM3jMnQBPMJPBwOh1HoXlbOSBxxbz6vUiwNszotkAiWuTdgpdrxX9XRQWhMRmqFljPaDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:53:00.438168Z"},"content_sha256":"f401b9551fd9ad0322a47e3a68199b96a19f48cc18935084459e6cc5c6e5a645","schema_version":"1.0","event_id":"sha256:f401b9551fd9ad0322a47e3a68199b96a19f48cc18935084459e6cc5c6e5a645"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XGSYIIV376KNSG2P5EF6GBHUBS/bundle.json","state_url":"https://pith.science/pith/XGSYIIV376KNSG2P5EF6GBHUBS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XGSYIIV376KNSG2P5EF6GBHUBS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T19:53:00Z","links":{"resolver":"https://pith.science/pith/XGSYIIV376KNSG2P5EF6GBHUBS","bundle":"https://pith.science/pith/XGSYIIV376KNSG2P5EF6GBHUBS/bundle.json","state":"https://pith.science/pith/XGSYIIV376KNSG2P5EF6GBHUBS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XGSYIIV376KNSG2P5EF6GBHUBS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:XGSYIIV376KNSG2P5EF6GBHUBS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9ff0b04d66b4adf3857698930b5240b312c5c0fc7c1b44e63c71e4c414526e4e","cross_cats_sorted":["cs.SD","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-01-14T02:03:01Z","title_canon_sha256":"e99a6286768f2c1f23441adb596a6e778bfefa4b2c5e4903003922eac346a367"},"schema_version":"1.0","source":{"id":"2001.04585","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.04585","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"arxiv_version","alias_value":"2001.04585v1","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.04585","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"pith_short_12","alias_value":"XGSYIIV376KN","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"XGSYIIV376KNSG2P","created_at":"2026-07-05T00:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"XGSYIIV3","created_at":"2026-07-05T00:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:f401b9551fd9ad0322a47e3a68199b96a19f48cc18935084459e6cc5c6e5a645","target":"graph","created_at":"2026-07-05T00:33:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2001.04585/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The x-vector maps segments of arbitrary duration to vectors of fixed dimension using deep neural network. Combined with the probabilistic linear discriminant analysis (PLDA) backend, the x-vector/PLDA has become the dominant framework in text-independent speaker verification. Nevertheless, how to extract the x-vector appropriate for the PLDA backend is a key problem. In this paper, we propose a Gaussian noise constrained network (GNCN) to extract xvector, which adopts a multi-task learning strategy with the primary task classifying the speakers and the auxiliary task just fitting the Gaussian ","authors_text":"Bin Gu, Wu Guo","cross_cats":["cs.SD","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-01-14T02:03:01Z","title":"Gaussian speaker embedding learning for text-independent speaker verification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.04585","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:83b732c12f645d81b5fc1e7ec4266900d370c387eefff470bcfe03ff0f21d27a","target":"record","created_at":"2026-07-05T00:33:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9ff0b04d66b4adf3857698930b5240b312c5c0fc7c1b44e63c71e4c414526e4e","cross_cats_sorted":["cs.SD","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-01-14T02:03:01Z","title_canon_sha256":"e99a6286768f2c1f23441adb596a6e778bfefa4b2c5e4903003922eac346a367"},"schema_version":"1.0","source":{"id":"2001.04585","kind":"arxiv","version":1}},"canonical_sha256":"b9a58422bbff94d91b4fe90be304f40cb70bf66ce24109cd0a04f4bd5281036b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b9a58422bbff94d91b4fe90be304f40cb70bf66ce24109cd0a04f4bd5281036b","first_computed_at":"2026-07-05T00:33:27.190281Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:33:27.190281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ss5x6tMlxbCAAipAFjYeW3n8mpVSKGH+k9cgiGWQCBk7aRpW35Lb8dO1IYDOt8PS2dbMMeUQjuu4DK+8t/QLCw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:33:27.190633Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.04585","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:83b732c12f645d81b5fc1e7ec4266900d370c387eefff470bcfe03ff0f21d27a","sha256:f401b9551fd9ad0322a47e3a68199b96a19f48cc18935084459e6cc5c6e5a645"],"state_sha256":"1798a05003a27b08ef3c13278ca2fe3065f88964021f3dff19b9f043f0f42ef9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k19Bv9zlRyNK4LeoK8+LtjyMD2lRTReszW6EcMSiX4Rfj5Gm86NJ/ej3IEQw2skMhFiONElT7pnXvmnsLXIlCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T19:53:00.442687Z","bundle_sha256":"70c27818f2a0a7c5687af1fc50f5027c9ce96e7852e38b07e98507051079a41e"}}